System and method for detecting an object

Abstract

A system for detecting an object from an image includes: a computing apparatus having a processing unit, a memory unit and a user interface, the processing unit operatively coupled to the memory unit, the computing apparatus configured to: compress optical signals (i.e., visual signals) from a real world scene using a Snapshot Compressive Imaging (SCI) system to obtain compressed signals, receive the compressed signals, store the compressed signals as compressed images, apply one or more knowledge distillation techniques in conjunction with a pre trained object detection model to detect one or more objects directly from each compressed image, utilize motion information encoded within the compressed data to optimize the object detection process, and present on the user interface the one or more detected objects on an image.

1 . A system for detecting an object from an image comprising:
 a computing apparatus comprising a processing unit, a memory unit and a user interface, the processing unit operatively coupled to the memory unit,   the computing apparatus is configured to:
 receive one or more images of a real-world scene, 
 compress the one or more received images to obtain one or more compressed images, 
 detect one or more objects in each compressed image, and; 
 present the one or more detected objects on a user interface. 
   
     
     
         2 . The system of  claim 1  wherein the computing apparatus is adapted to perform an object detection process directly on the compressed images to detect the one or more objects. 
     
     
         3 . The system of  claim 2  wherein the computing apparatus comprises an object detection model stored therein, wherein the computing apparatus is configured to apply the object detection model to the received images as part of the object detection process. 
     
     
         4 . The system of  claim 3  wherein the object detection model comprises a backbone feature module and a task loss module and feature loss module. 
     
     
         5 . The system of  claim 1  wherein the computing apparatus is configured to compress the received images using a Snapshot Compressive Imaging (SCI) system. 
     
     
         6 . The system of  claim 5  wherein the computing apparatus is configured to encode the received images by temporally varying masks as part of compressing the one or more received images. 
     
     
         7 . The system of  claim 3  wherein the object detection model comprises a pre trained YOLO model. 
     
     
         8 . The system of  claim 7  wherein the object detection model comprises an encoder, convolution layers, a backbone feature, neck and head, wherein neck and head output an image with detected objects identified thereon. 
     
     
         9 . The system of  claim 8  wherein the object detection model is trained using a knowledge distillation process executed by the computing apparatus. 
     
     
         10 . The system of claim of  claim 8  wherein computing apparatus is configured to, as part of the knowledge distillation process:
 build a teacher model configured to extract and utilize visual information from ground truth images or videos, 
 guide a student model using the teacher model to train the student model to detect objects, wherein the student model is the object detection model, and; 
 wherein the teacher model and the student model are adapted to utilize a combined feature loss and task loss. 
 
     
     
         11 . The system of  claim 1  wherein the one or more images are still images or frames of a video stream. 
     
     
         12 . A system for detecting an object from an image comprising:
 a computing apparatus comprising a processing unit, a memory unit and a user interface, the processing unit operatively coupled to the memory unit,   the computing apparatus is configured to:
 compress optical signals (i.e., visual signals) from a real world scene using a Snapshot Compressive Imaging (SCI) system to obtain compressed signals, 
 receive the compressed signals, 
 store the compressed signals as compressed images, 
 apply one or more knowledge distillation techniques in conjunction with a pre trained object detection model to detect one or more objects directly from each compressed image, 
 utilise motion information encoded within the compressed data to optimise the object detection process, 
 present on the user interface the one or more detected objects on an image. 
   
     
     
         13 . The system for detecting an object of  claim 12 , wherein the computing apparatus is configured to capture images using a snapshot compressive imaging (SCI) system, wherein the SCI system is configured to capture images and compress the images to generate the one or more compressed images. 
     
     
         14 . The system for detecting an object of  claim 13 , wherein the computing apparatus may be configured to apply an object detection model, wherein the object detection model is arranged to be trained by using the knowledge distillation process in conjunction with a pre trained model; and the pre trained model is arranged to operate as a teacher model to train the object detection model. 
     
     
         15 . The system for detecting an object of  claim 14 , wherein the pretrained student model may be a YOLO model. 
     
     
         16 . The system for detecting an object of  claim 14 , the one or more objects are detected directly in each compressed image, wherein the one or more objects are detected in each compressed image without first decompressing or reconstructing the images. 
     
     
         17 . The system for detecting an object of  claim 16 , wherein the object detection model comprises a feature loss module and a task loss module, and the object detection model comprises an encoder, convolution layers, a backbone feature, neck and head, wherein neck and head output an image with detected objects identified thereon. 
     
     
         18 . The system for detecting an object of  claim 14 , the object detection model is trained to identify objects and perform feature extraction from compressed images. 
     
     
         19 . A method for detecting an object comprising the steps of:
 receiving one or more images of a real-world scene,   compressing the one or more received images to obtain one or more compressed images, wherein compressing comprises applying a snapshot compressive imaging (SCI) system to compress the received images,   wherein compressing the images further comprises encoding the received images by temporally varying masks,   detecting one or more objects directly in each compressed image,   presenting the one or more detected objects on a user interface,   wherein detecting one or more objects comprises applying an object detection model to the received images, wherein the object detection model comprises a backbone feature module and a task loss module and feature loss module,   wherein the object detection model is a pretrained YOLO model, and wherein the YOLO model is pretrained to detect objects directly in each compressed image.   
     
     
         20 . The method of  claim 15 , employing a combination of feature loss and task loss in the training strategy of the detection model, which is arranged to enhance the performance of object detection algorithms that work directly with compressed optical measurements, and wherein the training strategy is arranged to align with real-time application requirements to overcome limitations associated with traditional methods that require decompression or reconstruction of data before detection can occur.